2114-P: Artificial Intelligence–Assisted Identification of Severe Hypoglycemia Risk Factors in Pediatric Type 1 Diabetes

Introduction and Objective: Severe hypoglycemia (SH) is a life-threatening complication of type 1 diabetes (T1D), making accurate risk identification essential. We used artificial intelligence (AI)-based chart review to improve identification of factors associated with SH in children with T1D. Methods: We studied children (<18y) with T1D seen in endocrinology clinics at a large tertiary health center between April 2020 and April 2025 (n = 4,018). Clinicians were required to screen for SH at each visit using questionnaires built into the medical chart. Data scientists employed a hybrid AI-assisted and traditional approach to extract clinical, socioeconomic, and CGM data using PySpark transformations, ChatGPT 5.1 within Palantir Foundry, and custom Python scripts. Groups with and without SH were compared using Fisher's exact, χ² and Mann-Whitney tests. Select multivariate regressions were performed. Results: Clinician-documented SH occurred in 5% of patients (206/4018). Table 1 presents group comparisons. Controlling for insurance type, Black race was no longer significantly associated with SH (p=0.07). Conclusion: AI-assisted analyses of SH reflect established disparities in glycemic control and diabetes technology use, possibly associated with insurance coverage. Future AI-generated predictive models may improve risk assessment and guide focused interventions. Disclosure N. Borrego: None. D. Palacios: None. H. Chen: None. Z. Liu: None. D.J. DeSalvo: Consultant; Current; Dexcom, Inc., Insulet Corporation. Advisory Panel; Current; Sanofi. Research Support; Current; Medtronic. R.S. Aguirre: Advisory Panel; Ended; Sanofi. Research Support; Current; Sanofi. M.J. Redondo: Advisory Panel; Current; Sanofi. Other - Data Safety Monitoring committee; Current; Lilly. M. Tosur: None. M.L. Ferm: Advisory Panel; Ended; Rezolute Inc.